Robust Optimization and Kriging Metamodeling of Deep-Drawing Process to Obtain a Regulation Curve of Blank Holder Force

Abstract

In recent decades, the automotive industry has had a constant evolution with consequent enhancement of products quality. In industrial applications, quality may be defined as conformance to product specifications and repeatability of manufacturing process. Moreover, in the modern era of Industry 4.0, research on technological innovation has made the real-time control of manufacturing process possible. Moving from the above context, a method is proposed to perform real-time control of a deep-drawing process, using the stamping of the upper front cross member of a car chassis as industrial case study. In particular, it is proposed to calibrate the force acting on the blank holder, defining a regulation curve that considers the material yield stress and the friction coefficient as the main noise variables of the process. Firstly, deep-drawing process was modeled by using commercial Finite Element (FE) software AutoForm. By means of AutoForm Sigma tool, the stability and capability of deep-drawing process were analyzed. Numerical results were then exploited to create metamodels, by using the kriging technique, which shows the relationships between the process parameters and appropriate quality indices. Multi-objective optimization with a desirability function was carried out to identify the optimal values of input parameters for deep-drawing process. Finally, the desired regulation curve was obtained by maximizing total desirability. The resulting regulation curve can be exploited as a useful tool for real-time control of the force acting on the blank holder.

Robust control of the draw-in in a deep-drawing process: a measurement-based approach

“…The action of the blank holder can prevent defects on the drawn component such as cracks, wrinkles and thinning [1][2]. Therefore, one of the design parameters affecting the drawing process is the force on the blank holder [3][4]. In fact, it has been shown that an increase in the force on the blank holder can lead to a reduction in wrinkles.…”

“…The quality of the deep-drawn component, however, can also be affected by the process variability due, for example, to the lubrication conditions and to the properties of the material which can change depending on the supplier or the coil adopted [3]. Positioning of the blank is another noise parameter that could affect the quality of the final drawn component [5].…”

A Method for Simultaneous Optimization of Blank Shape and Forming Tool Geometry in Sheet Metal Forming Simulations

“…Owing to the many difficulties that arise from increasing nonlinearity, adjustment direction, and surface description, the classical approach is to optimize the process parameters, such as blank-holding force, the location and shape of the holder, selective lubrication, tool parts, and kinematics. For some examples, please see [25,26], and recently [27][28][29]. Since these problems are often solved through optimization algorithms, a large number of computer simulations of the forming process are needed.…”

Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

“…In the literature, machine learning algorithms have been already applied to various manufacturing topics, such as for the prediction of joint strength of ultrasonic welding processes [22], to estimate the tool wear in milling operations [23], to diagnose the dimensional variation of additive manufactured parts [24], to classify the cutting phase of the natural fiber reinforced plastic composites [25] and to predict the tool life in the micro-milling process [26]. More recently, Wang et al [27] developed a deep learning-based algorithm for the recognition of the defects in the strip rolling process, Marques et al [28] investigated the performances of parametric and non-parametric models for the correlation of process and material variables to springback and wall thinning, Palmieri et al [29] defined a metamodel to correlate the process parameters and key-quality indicators for the optimization of the blank-holding forces in the stamping process, and Winiczenko [30] utilized a hybrid response surface methodology combined with a genetic algorithm to simulate and optimize the friction welding parameters in AISI 1020-ASTM A536 joints.…”